Group S: Nutrition Intake
Renfang Yang, Yixuan Li, Huan Sun
2022/4/28
1. Nutrition Intake Overview
1.1 Worldwide Discussion
Here we generate a word cloud from tweets which is under the tag “nutritional”, and we would further see if there is a difference between the general tag and the tag for a specific type of food using this word cloud later.
#word cloud with "nutritional" tag in twitter
n <- "data/nutritional"
files <- list.files(n, pattern="csv", full.names=TRUE) %>%
set_names()
wc_n <- files %>% map_dfr(read_csv, .id="filename")
n_text <- str_c(wc_n$data.text, collapse = "")
#clean the text
n_text <-
n_text %>%
str_remove("\\n") %>% # remove linebreaks
rm_twitter_url() %>% # Remove URLS
rm_url() %>%
str_remove_all("#\\S+") %>% # Remove any hashtags
str_remove_all("@\\S+") %>% # Remove any @ mentions
removeWords(stopwords("english")) %>% # Remove common words (a, the, it etc.)
removeNumbers() %>%
stripWhitespace() %>%
removeWords(c("amp")) %>%
removeWords(stopwords("SMART")) %>%
removePunctuation()
removeNumPunct <- function(x){gsub("[^[:alpha:][:space:]]*", "", x)}
# Convert the data into a summary table
n_Corpus <-
Corpus(VectorSource(n_text)) %>%
tm_map(content_transformer(removeNumPunct)) %>%
TermDocumentMatrix() %>%
as.matrix()
n_Corpus <- sort(rowSums(n_Corpus), decreasing=TRUE)
n_Corpus <- data.frame(word = names(n_Corpus), freq=n_Corpus, row.names = NULL)
#wordcloud
set.seed(1000)
wordcloud2(n_Corpus, color = 'random-light', size = 2, minRotation = -pi/6, maxRotation = -pi/6)1.2 Top Food Worldwide (by average daily intake)
top_food_box <- food_life %>%
filter(year==2018,age==999,female==999,urban==999,edu==999) %>%
ggplot(.,aes(x=reorder(Food,intake), y=intake))+
geom_boxplot( aes(color=Food,group=Food,fill=Food),alpha = 0.7)+
labs(title="Food Intake Worldwide",
caption= "Source:Global Dietary Database",
y="Average Food Daily Intake(g/d)",x='')+
# theme(legend.position='none')+
theme_pander()+
coord_flip()
ggplotly(top_food_box)1.3 Top Food Worldwide by Sex
top_food_sex <- food_life %>%
filter(year==2018,age==999,female!=999,urban==999,edu==999) %>%
mutate(sex=if_else(female==1,'female','male'))%>%
group_by(Food,sex)%>%
summarise(intake_avg = round(mean(intake),2))%>%
arrange(desc(intake_avg))
ggplot(top_food_sex,aes(x=reorder(Food,intake_avg), y=intake_avg,fill=sex)) +
geom_bar(stat="identity",position="dodge", width = 0.5, alpha = 0.7) +
labs(title="Differences in Food Intake for Female & Male",
caption= "Source:Global Dietary Database",
y="Average Food Daily Intake",x='')+
theme_pander()+
coord_flip()
1.4 Food Intake Worldwide by Year
# Average Food Intake Worldwide by Year
top_year <- food_life %>%
filter(age==999,female==999,urban==999,edu==999) %>%
group_by(year,Food)%>%
summarise(intake_avg = round(mean(intake),2))%>%
arrange(desc(intake_avg))
plot1 <- top_year%>%
ggplot(., aes(x = year, y=intake_avg,group=Food,color=Food)) +
geom_line( size=1)+
geom_point(size=1)+
labs(x='Year', y='Intake Avg(g/day)', title='Average Food Intake Worldwide')+
theme_pander()
ggplotly(plot1)2. Dietary Structure
Below we visualize the dietary structure in 1990 of 9 countries with top and last total GDP. The dietary structure is shown by calculating the percentage of each type of food in the total food intake respectively.
#Dietary Structure of 10 countries in 1990
# select food intake data in 10 countries
countries_10 <- filter(food_life, iso3 %in% country_list)
#select the general data points (all demographics features = 999)
gen_countries <- countries_10 %>% filter(age == 999 &
female == 999,
urban == 999,
edu == 999)
#Dietary Structure 1990
d_stru_90 <- gen_countries %>% filter(year==1990) %>%
group_by(Country) %>%
summarise(Food, total = sum(intake), percentage=(round(intake/total,4))*100) %>%
ggplot(aes(fill=Food, y=percentage, x=Country)) +
geom_bar(position="stack", stat="identity") +
labs(x="Country", title="Dietary Sturcture in 1990") +
theme_classic() +
coord_flip()
ggplotly(d_stru_90) %>% layout(xaxis = list(title = "Percentage in Diet"),
legend = list(orientation = "h",itemsizing="constant"))Then we visualize the dietary structure in 2018 of the same 9 countries.
#Dietary Structure of 10 countries in 2018
d_stru_18 <- gen_countries %>% filter(year==2018) %>%
group_by(Country) %>%
summarise(Food, total = sum(intake), percentage=(round(intake/total,4))*100) %>%
ggplot(aes(fill=Food, y=percentage, x=Country)) +
geom_bar(position="stack", stat="identity") +
labs(x="Country", title="Dietary Sturcture in 2018") +
theme_classic() +
coord_flip()
ggplotly(d_stru_18) %>% layout(xaxis = list(title = "Percentage in Diet"),
legend = list(orientation = "h",itemsizing="constant"))2.1 Dietary Struture Changes in China
After comparing the dietary structure in 1990 and in 2018, we find that Chinese’s dietary structure has experienced the most significant changes, therefore, we pay attention to the dietary structure based on demographic characteristics in China. We firstly seperate the dietary structure by area.
#Changes of Dietary Structure (1990 vs 2018) in rural China
area <- countries_10 %>% filter(age == 999 & urban != 999 &
edu == 999 & Country == "China" &
female == 999 & year != 2020) %>%
mutate(area= case_when(urban==1 ~ "Urban",
urban==0 ~ "Rural"))
area_China <- area %>%
group_by(year) %>%
summarise(Food, area, total = sum(intake), percentage=(round(intake/total,4))*100, .groups = 'drop')
ru.mid<-ggplot(area_China,aes(x=1,y=Food))+geom_text(aes(label=Food))+
geom_segment(aes(x=0.94,xend=0.96,yend=Food))+
geom_segment(aes(x=1.04,xend=1.065,yend=Food))+
ggtitle("")+
ylab(NULL)+
scale_x_continuous(expand=c(0,0),limits=c(0.94,1.065))+
theme(axis.title=element_blank(),
panel.grid=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
panel.background=element_blank(),
axis.text.x=element_text(color=NA),
axis.ticks.x=element_line(color=NA),
plot.margin = unit(c(1,-1,1,-1), "mm"))
ru_90 <- area_China %>%
filter(area=="Rural" & year==1990) %>%
ggplot(aes(x = Food, y = percentage,fill = Food)) +
geom_bar(stat = "identity") + ggtitle("Dietary Structure(Rural China,1990)") +
geom_text(aes(y=10, label = percentage),color = "black") +
coord_flip() + scale_y_reverse() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position="none",
panel.background=element_blank(),
plot.title = element_text(size = 10),
plot.margin = unit(c(1,-1,1,0), "mm"))
ru_18 <- area_China %>%
filter(area=="Rural" & year==2018) %>%
ggplot(aes(x = Food, y = percentage, fill = Food)) +
geom_bar(stat = "identity") + ggtitle("Dietary Structure(Rural China,2018)") +
geom_text(aes(y=10, label = percentage),color = "black") +
coord_flip() + xlab(NULL) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position="none",
panel.background=element_blank(),
plot.title = element_text(size = 10),
plot.margin = unit(c(1,-1,1,0), "mm"))
ru1 <- ggplot_gtable(ggplot_build(ru_90))
ru2 <- ggplot_gtable(ggplot_build(ru_18))
ru.mid <- ggplot_gtable(ggplot_build(ru.mid))
grid.arrange(ru1,ru.mid,ru2,ncol=3,widths=c(3/9,3/9,3/9))
#Changes of Dietary Structure (1990 vs 2018) in urban China
ur_90 <- area_China %>%
filter(area=="Urban" & year==1990) %>%
ggplot(aes(x = Food, y = percentage,fill = Food)) +
geom_bar(stat = "identity") + ggtitle("Dietary Structure(Urban China,1990)") +
geom_text(aes(y=10, label = percentage),color = "black") +
coord_flip() + scale_y_reverse() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position="none",
panel.background=element_blank(),
plot.title = element_text(size = 10),
plot.margin = unit(c(1,-1,1,0), "mm"))
ur_18 <- area_China %>%
filter(area=="Urban" & year==2018) %>%
ggplot(aes(x = Food, y = percentage, fill = Food)) +
geom_bar(stat = "identity") + ggtitle("Dietary Structure(Urban China,2018)") +
geom_text(aes(y=10, label = percentage),color = "black") +
coord_flip() + xlab(NULL) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position="none",
panel.background=element_blank(),
plot.title = element_text(size = 10),
plot.margin = unit(c(1,-1,1,0), "mm"))
library(gridExtra)
ur1 <- ggplot_gtable(ggplot_build(ur_90))
ur2 <- ggplot_gtable(ggplot_build(ur_18))
grid.arrange(ur1,ru.mid,ur2,ncol=3,widths=c(3/9,3/9,3/9))
Based on above graphs, there is no significant difference of dietary structure between the rural and urban area in China. Both of the areas see the same changes in dietary structure.
Among all the types of food, two of them experienced significant changes– Refined grains and Non-starchy vegetables. Therefore, we mainly focus in the changes of these two types of food in China. The dimension we choose to see the difference is gender.
# Changes of Refined Grains by Gender in China
gender <- countries_10 %>%
filter(age == 999 & urban == 999 & edu == 999 & Country == "China" &
female != 999 & year != 2020) %>%
mutate(gender= case_when(female==1 ~ "Female",
female==0 ~ "Male"))
gender_China <- gender %>%
group_by(year) %>%
summarise(Food, gender, total = sum(intake), percentage=(round(intake/total,4))*100)
rg_gender <- gender_China %>%
filter(Food == "Refined grains") %>%
ggplot(aes(x = year, y=percentage, group=gender, color=gender)) +
geom_line( size=1) +
geom_point(size=1) +
labs(x="Year", y="Percentage of Refined Grains", title="Refined Grains in Dietary Structure by Gender in China") +
theme_classic()
ggplotly(rg_gender) We can see there is a similar descending trend in the changes of the percentage of refined grains between females and males in China.
#Changes of Non-starchy Vegetables by Gender in China
nv_gender <- gender_China %>%
filter(Food == "Non-starchy vegetables") %>%
ggplot(aes(x = year, y=percentage, group=gender, color=gender)) +
geom_line( size=1) +
geom_point(size=1) +
labs(x="Year", y="Percentage of Non-starchy vegetables", title="Non-starchy vegetables in Dietary Structure by Gender in China") +
theme_classic()
ggplotly(nv_gender) There is a similar ascending trend in the changes of the percentage of non-starchy vegetables between females and males in China, while the gap between females and males is growing larger among these 28 years.
2.2 Refined Grains
Compared with the word cloud for #nutritional tag, there is no significant differences between the words under the #refineddgrains tag. Refined grains are not taken as a negative type of food in twitter as we expected.
# Refined Grains word cloud in twitter
g <- "data/grains"
files_g <- list.files(g, pattern="csv", full.names=TRUE) %>%
set_names()
wc_g <- files_g %>% map_dfr(read_csv, .id="filename")
g_text <- str_c(wc_g$data.text, collapse = "")
#clean the text
g_text <-
g_text %>%
str_remove("\\n") %>% # remove linebreaks
rm_twitter_url() %>% # Remove URLS
rm_url() %>%
str_remove_all("#\\S+") %>% # Remove any hashtags
str_remove_all("@\\S+") %>% # Remove any @ mentions
removeWords(stopwords("english")) %>% # Remove common words (a, the, it etc.)
removeNumbers() %>%
stripWhitespace() %>%
removeWords(c("amp")) %>%
removeWords(stopwords("SMART")) %>%
removePunctuation()
removeNumPunct <- function(x){gsub("[^[:alpha:][:space:]]*", "", x)}
# Convert the data into a summary table
g_Corpus <-
Corpus(VectorSource(n_text)) %>%
tm_map(content_transformer(removeNumPunct)) %>%
TermDocumentMatrix() %>%
as.matrix()
g_Corpus <- sort(rowSums(g_Corpus), decreasing=TRUE)
g_Corpus <- data.frame(word = names(g_Corpus), freq=g_Corpus, row.names = NULL)
#wordcloud
wordcloud2(g_Corpus, color = 'random-light', size = 2, minRotation = -pi/6, maxRotation = -pi/6)Refined Grains Intake Over the World
# read the refined grains data in
refined_grains <- read.csv("data/Country-level-estimates/v07_cnty.csv")
refined <- read.csv("data/Country-level-estimates/v07_cnty.csv")%>%
filter(year==2018,female!=999,age==999,urban==999,edu==999)%>%
dplyr::select(iso3,female,median)# being shown as median.x in the sp file
refined_female<-refined%>%
filter(female==1)%>%
dplyr::select(iso3,median)
# being shown as median.y
refined_male <- refined%>%
filter(female==0)%>%
dplyr::select(iso3,median)
# get the world map shape files with rgdal
library(rgdal)
world_sp = readOGR(dsn= "data/world_map",
layer="TM_WORLD_BORDERS_SIMPL-0.3")## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\alva\Documents\GitHub\Group_S_Nutrition\data\world_map", layer: "TM_WORLD_BORDERS_SIMPL-0.3"
## with 246 features
## It has 11 fields
## Integer64 fields read as strings: POP2005combined <- world_sp@data %>%
left_join(refined_female,by=c(ISO3="iso3"))%>%
left_join(refined_male,by=c(ISO3="iso3"))
world_sp@data <- combinedPlot the refined grains consumption given sex.
plot1 <- leaflet(world_sp,
leafletOptions(attributionControl = FALSE, minzoom=1.5)) %>%
setView(lat=10, lng=0 , zoom=2) %>%
# Base groups = Background layer
addTiles(group = "OpenStreetMap") %>%
addProviderTiles(providers$Stamen.TonerLite, group = "Toner Lite") %>%
# add country borders
addPolygons(group='Female Refined Grains Intake',
stroke = TRUE, smoothFactor = 0.5,
weight=1, color='#333333', opacity=1,
# add first layer:
fillColor = ~colorQuantile("Blues", median.x)(median.x),
fillOpacity = 1,
# add label
label = ~stringr::str_c(NAME, ' ',
formatC(median.x, big.mark = ',', format='d')),
labelOptions = labelOptions(direction = 'auto'))%>%
# add the second layer:
addPolygons(group='Male Refined Grains Intake',
stroke = TRUE, smoothFactor = 0.5,
weight=1, color='#333333', opacity=1,
fillColor = ~colorQuantile("Reds",median.y)(median.y), fillOpacity = 1,
# add label
label = ~stringr::str_c(NAME, ' ',
formatC(median.x, big.mark = ',', format='d')),
labelOptions = labelOptions(direction = 'auto'))%>%
# add layer control
addLayersControl(
baseGroups = c("OpenStreetMap", "Toner Lite"), #<<
overlayGroups = c("Female Refined Grains Intake","Male Refined Grains Intake"), #<<
options = layersControlOptions(collapsed = TRUE) )
#frameWidget(plot1)
widgetframe::frameWidget(plot1, width = "100%")The maps suggests that worldwide wise, there’s no obvious difference in refined grains intake upon gender. But the distribution of densely darkened countries suggests that there’s may have some insights super-regional wise.
# Refined Grains bar chart by continent
df <- refined_grains%>%
filter(age==999,female==999,urban==999,edu==999)%>%
dplyr::select(superregion2,iso3,year,median)%>%
group_by(superregion2,year)%>%
mutate(regional_avg=mean(median))%>%
select(superregion2,year,regional_avg)%>%
mutate(superregion2 = recode(superregion2, Asia = "East & Southeast Asia",
FSU = "Former Soviet Union",
HIC = "High-Income Countries",
LAC = "Latin America & Caribbean",
MENA= "Middle East & North Africa",
SAARC ="South Asia",
SSA = "Sub-Saharan Africa"
))
df%>%
ggplot(aes(x = year, y = regional_avg,
color = factor(superregion2,levels=c("East & Southeast Asia",
"Former Soviet Union",
"High-Income Countries",
"Latin America & Caribbean",
"Middle East & North Africa",
"South Asia",
"Sub-Saharan Africa")))) +
geom_point(size = 2, alpha = 0.5)+
geom_smooth(se = F, alpha=0.3) +
scale_color_manual(values = c("#67001f", "#d6604d",
"#fddbc7", "#d1e5f0", "#92c5de",
"#2166ac", "#053061"))+
guides(color=guide_legend(title=NULL))+
theme_minimal() +
labs(x = "Year", y = "Grams") +
scale_x_continuous(breaks = seq(1990, 2018, 5), guide = guide_axis(angle = 0))+
theme(axis.title.y = element_text(vjust = 2),
legend.direction = "horizontal",
legend.justification = 0.05,
legend.position = "top",
legend.text = element_text(size = 6, color = "gray10"),
panel.grid.minor.y =element_blank()) +
ggtitle("Superregional Refined Grains Average Intake") +
labs(caption = "*For all ages, gender, rural/urban, edu background.")
Takeaway: Throughout the year from 1990 to 2015, the High Income Countries have shown a significantly lower consumption of refined grains compared to other super regional areas. Both South and East&Southeast Asia consumes the highest amount of refined grains.
2.3 Non-starchy Vegetables
There are two main categories of vegetables: starchy and non-starchy. Starchy types include potatoes, corn and beans, while non-starchy types include broccoli, tomatoes and zucchini,etc. Both types of veggies are rich in fiber and nutrients, but starchy vegetables are known with higher percentage of carbs and calories.
Note the unit for the survey data of non starchy vegetables is grams per day.
Here we try to calculate the female respondents’ average intake of non starchy vegetables in 2018.
The code book suggests that the value being 999 means that the query variable of age, edu, urban includes no breakdown.
# get the data in
non_starchy_vegs<-read.csv("data/Country-level-estimates/v02_cnty.csv")
starchy_vegs<-read.csv("data/Country-level-estimates/v04_cnty.csv")
female_nsv_18<- non_starchy_vegs %>%
# choose the year of 2018 for present
filter(year==2018,age==999,edu==999,urban==999,female==1)%>%
dplyr::select(iso3,median)%>%
arrange(desc(median))%>%
rename(non_starchy_vegs_mean=median)
female_sv_18<- starchy_vegs %>%
filter(year==2018,age==999,edu==999,urban==999,female==1)%>%
# can add a filter of gender, with two layers
dplyr::select(iso3,median)%>%
arrange(desc(median))%>%
rename(starchy_vegs_mean=median)
# add selected data from the dataset back to shape-files
combined <- world_sp@data %>%
left_join(female_nsv_18,by=c(ISO3="iso3"))%>%
left_join(female_sv_18,by=c(ISO3="iso3"))
world_sp@data <- combinedplot2 <- leaflet(world_sp,
leafletOptions(attributionControl = FALSE, minzoom=1.5)) %>%
setView(lat=10, lng=0 , zoom=2) %>%
# Base groups = Background layer
addTiles(group = "OpenStreetMap") %>%
addProviderTiles(providers$Stamen.TonerLite, group = "Toner Lite") %>%
# add country borders
addPolygons(group='Non-strachy Vegetables',
stroke = TRUE, smoothFactor = 0.5,
weight=1, color='#333333', opacity=1,
# add first layer:
fillColor = ~colorQuantile("Blues", non_starchy_vegs_mean)(non_starchy_vegs_mean),
fillOpacity = 1,
# add label
label = ~stringr::str_c(NAME, ' ',
formatC(non_starchy_vegs_mean, big.mark = ',', format='d')),
labelOptions = labelOptions(direction = 'auto'))%>%
# add the second layer:
addPolygons(group='Strachy Vegetables',
stroke = TRUE, smoothFactor = 0.5,
weight=1, color='#333333', opacity=1,
fillColor = ~colorQuantile("Reds",starchy_vegs_mean)(starchy_vegs_mean), fillOpacity = 1,
# add label
label = ~stringr::str_c(NAME, ' ',
formatC(starchy_vegs_mean, big.mark = ',', format='d')),
labelOptions = labelOptions(direction = 'auto'))%>%
# add layer control
addLayersControl(
baseGroups = c("OpenStreetMap", "Toner Lite"), #<<
overlayGroups = c("Non-strachy Vegetables","Strachy Vegetables"), #<<
options = layersControlOptions(collapsed = TRUE) )
frameWidget(plot2)Takeaway: For starchy vegetables, South America, West Asia and Middle Africa countries had an overall higher consumption, and North America and Australia have a relatively low consumption compared to that.
Yet for non-starchy vegetables, Asian women have an significantly and obvious higher intake than the rest of the world. Women in Middle Africa also have this pattern of food intake.
# Vegetables bar chart by continent
df01 <- non_starchy_vegs%>%
filter(age==999,female==999,urban==999,edu==999)%>%
dplyr::select(superregion2,iso3,year,median)%>%
group_by(superregion2,year)%>%
mutate(regional_avg=mean(median))%>%
select(superregion2,year,regional_avg)%>%
mutate(superregion2 = recode(superregion2, Asia = "East & Southeast Asia",
FSU = "Former Soviet Union",
HIC = "High-Income Countries",
LAC = "Latin America & Caribbean",
MENA= "Middle East & North Africa",
SAARC ="South Asia",
SSA = "Sub-Saharan Africa"
))
df01%>%
ggplot(aes(x = year, y = regional_avg,
color = factor(superregion2,levels=c("East & Southeast Asia",
"Former Soviet Union",
"High-Income Countries",
"Latin America & Caribbean",
"Middle East & North Africa",
"South Asia",
"Sub-Saharan Africa")))) +
geom_point(size = 2, alpha = 0.5)+
geom_smooth(se = F, alpha=0.3) +
scale_color_manual(values = c("#67001f", "#d6604d",
"#fddbc7", "#d1e5f0", "#92c5de",
"#2166ac", "#053061"))+
guides(color=guide_legend(title=NULL))+
theme_minimal() +
labs(x = "Year", y = "Grams") +
scale_x_continuous(breaks = seq(1990, 2018, 5), guide = guide_axis(angle = 0))+
theme(axis.title.y = element_text(vjust = 2),
legend.direction = "horizontal",
legend.justification = 0.05,
legend.position = "top",
legend.text = element_text(size = 8, color = "gray10"),
panel.grid.minor.y =element_blank()) +
ggtitle("Superregional Non Starchy Vegetables Average Intake") +
labs(caption = "*For all ages, gender, rural/urban, edu background.")
Takeaway: Latin America & Caribbean countries has the lowest intake of non starchy vegetables, and surprisingly, the high income countries also shows a relatively low non starchy vegetables consumption. Russia (Former Soviet Union) has an increasing level of intake that allows it outnumbered other countries by the end of 2018.
3. Nutrition & Health
3.1 Food Intake & Life Expetancy
# life exp map# lm reg line chart
food_life2018 <- food_life %>%
filter(age==999,female==999,urban==999,edu==999,year==2018)
p7<- food_life2018 %>%
filter(varnum==7)%>%
ggplot(., aes(x = intake, y=life_exp)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Life expectancy', title="Refined grains")+
theme_classic()
p2 <- food_life2018 %>%
filter(varnum==2)%>%
ggplot(., aes(x = intake, y=life_exp)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Life expectancy', title="Non-starchy vegetables")+
theme_classic()
p1 <- food_life2018 %>%
filter(varnum==1)%>%
ggplot(., aes(x = intake, y=life_exp)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Life expectancy', title="Fruits")+
theme_classic()
p3 <- food_life2018 %>%
filter(varnum==3)%>%
ggplot(., aes(x = intake, y=life_exp)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Life expectancy', title="Potatoes")+
theme_classic()
p8 <- food_life2018 %>%
filter(varnum==8)%>%
ggplot(., aes(x = intake, y=life_exp)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Life expectancy', title="Whole grains")+
theme_classic()
p9 <- food_life2018 %>%
filter(varnum==9)%>%
ggplot(., aes(x = intake, y=life_exp)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Life expectancy', title="Processed meats")+
theme_classic()
life_food = ggarrange(p7,p2,p1,p3,p8,p9,ncol = 2, nrow = 3)
annotate_figure(life_food,top = text_grob("Life Expectancy & Food Intake",
color = "#ef8a62", face = "bold", size = 14))
3.1 Food Intake & Cancer Rate
# cancer rate mapcancer7<- food_life2018 %>%
filter(varnum==7)%>%
ggplot(., aes(x = intake, y=cancer_rate)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Cancer Rate', title="Refined grains")+
theme_classic()
cancer2 <- food_life2018 %>%
filter(varnum==2)%>%
ggplot(., aes(x = intake, y=cancer_rate)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Cancer Rate', title="Non-starchy vegetables")+
theme_classic()
cancer1 <- food_life2018 %>%
filter(varnum==1)%>%
ggplot(., aes(x = intake, y=cancer_rate)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Cancer Rate', title="Fruits")+
theme_classic()
cancer3 <- food_life2018 %>%
filter(varnum==3)%>%
ggplot(., aes(x = intake, y=cancer_rate)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Cancer Rate', title="Potatoes")+
theme_classic()
cancer8 <- food_life2018 %>%
filter(varnum==8)%>%
ggplot(., aes(x = intake, y=cancer_rate)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Cancer Rate', title="Whole grains")+
theme_classic()
cancer9 <- food_life2018 %>%
filter(varnum==9)%>%
ggplot(., aes(x = intake, y=cancer_rate)) +
geom_point(size=1,color='turquoise3')+
geom_smooth(method='lm', formula= y~x,color='tomato')+
labs(x='intake', y='Cancer Rate', title="Processed meats")+
theme_classic()
cancer_food = ggarrange(cancer7,cancer2,cancer1,cancer3,cancer8,cancer9,
ncol = 2, nrow = 3)
annotate_figure(cancer_food,top = text_grob("Cancer Rate & Food Intake",
color = "#ef8a62", face = "bold", size = 14))
Appendix
basic information of food intake
basic_intake <- food_life %>%
filter(age==999,female==999,urban==999,edu==999) %>%
rename(continent = superregion2) %>%
mutate_at(c('intake'), function(x) round(x,2))%>%
select(continent,year,Country,Food,intake) %>%
arrange(Country)
datatable(basic_intake,filter = list(position = "top"),rownames = FALSE) basic information of life expectancy & cancer rate
basic_life <- food_life %>%
filter(age==999,female==999,urban==999,edu==999) %>%
rename(continent = superregion2) %>%
mutate_at(c('cancer_rate','life_exp'), function(x) round(x,2))%>%
select(continent,year,Country,life_exp,cancer_rate) %>%
arrange(Country)
datatable(basic_life,filter = list(position = "top"),rownames = FALSE)